def generate_tv_cloud(search_params,
min_length,
stopwords,
ids=None,
stems=False):
"""Generates multiple document word clouds using the termvector approach"""
burst = True
chunk_size = 1000
progress = 0
wordcloud_counter = Counter()
if not ids:
# Normal (non-time line) wordcloud (based on query)
burst = False
result = count_search_results(settings.ES_INDEX, settings.ES_DOCTYPE,
search_params['query'],
search_params['dates'],
search_params['distributions'],
search_params['article_types'],
search_params['pillars'])
doc_count = result.get('count')
info = {'current': 0, 'total': doc_count}
current_task.update_state(state='PROGRESS', meta=info)
for subset in document_id_chunks(
chunk_size, settings.ES_INDEX, settings.ES_DOCTYPE,
search_params['query'], search_params['dates'],
search_params['distributions'], search_params['article_types'],
search_params['pillars']):
result = termvector_wordcloud(settings.ES_INDEX,
settings.ES_DOCTYPE, subset,
min_length, stems)
wordcloud_counter = wordcloud_counter + result
progress += len(subset)
info = {'current': progress, 'total': doc_count}
current_task.update_state(state='PROGRESS', meta=info)
else:
# Time line word cloud (based in list of document ids)
for subset in utils.chunks(ids, chunk_size):
result = termvector_wordcloud(settings.ES_INDEX,
settings.ES_DOCTYPE, subset,
min_length, stems)
wordcloud_counter = wordcloud_counter + result
progress += len(subset)
info = {'current': progress, 'total': len(ids)}
current_task.update_state(state='PROGRESS', meta=info)
return counter2wordclouddata(wordcloud_counter, burst, stopwords)
def handle(self, *args, **options):
print 'Emptying table...'
Term.objects.all().delete()
for timeframe, dates in TIMEFRAMES.items():
print 'Retrieving documents for timeframe {}...'.format(timeframe)
exclude_dist = Distribution.objects.exclude(
name='Landelijk').values_list('id', flat=True)
date_range = daterange2dates(dates)
total_documents = count_search_results(settings.ES_INDEX,
settings.ES_DOCTYPE, None,
date_range, exclude_dist,
[], []).get('count')
print 'Total documents: {}'.format(total_documents)
sets = document_id_chunks(10000,
settings.ES_INDEX,
settings.ES_DOCTYPE,
None,
date_range,
dist=exclude_dist)
print 'Counting terms...'
counter = Counter()
for n, s in enumerate(sets):
start_time = time.time()
counter += termvector_wordcloud(settings.ES_INDEX,
settings.ES_DOCTYPE,
s,
min_length=2,
add_freqs=False)
print 'Completed set {} in {} seconds...'.format(
n + 1,
time.time() - start_time)
print 'Calculating IDFs...'
terms = []
for term, count in counter.items():
if count > 1: # don't add single occurrences
idf = math.log10(total_documents / float(count))
terms.append(
Term(timeframe=timeframe,
word=term,
count=count,
idf=idf))
print 'Transferring to database...'
Term.objects.bulk_create(terms, batch_size=10000)
print 'Creating RecordDAWG'
d = dawg.RecordDAWG(
'<d', zip([t.word for t in terms], [(t.idf, ) for t in terms]))
d.save(os.path.join(settings.PROJECT_PARENT, timeframe + '.dawg'))
""" Test code below.
def generate_tv_cloud(search_params, min_length, stopwords, date_range=None, stems=False, idf_timeframe=''):
"""
Generates multiple document word clouds using the termvector approach.
"""
# Date range is either provided (in case of burst clouds from the timelines) or from the Query
dates = date_range or search_params['dates']
# First, count the search results
result = count_search_results(settings.ES_INDEX,
settings.ES_DOCTYPE,
search_params['query'],
dates,
search_params['exclude_distributions'],
search_params['exclude_article_types'],
search_params['selected_pillars'])
doc_count = result.get('count')
update_task_status(0, doc_count)
# Then, create the word clouds per chunk
progress = 0
wordcloud_counter = Counter()
for subset in document_id_chunks(settings.QUERY_DATA_CHUNK_SIZE,
settings.ES_INDEX,
settings.ES_DOCTYPE,
search_params['query'],
dates,
search_params['exclude_distributions'],
search_params['exclude_article_types'],
search_params['selected_pillars']):
wordcloud_counter += termvector_wordcloud(settings.ES_INDEX,
settings.ES_DOCTYPE,
subset,
min_length,
stems)
# Update the task status
progress += len(subset)
update_task_status(progress, doc_count)
# Remove non-frequent words form the counter
for key, count in dropwhile(lambda c: c[1] > math.log10(doc_count), wordcloud_counter.most_common()):
del wordcloud_counter[key]
# Remove the stopwords from the counter
for sw in stopwords:
del wordcloud_counter[sw]
# Return a dictionary with the results
return {
'result': normalize_cloud(wordcloud_counter, idf_timeframe),
'status': 'ok',
'burstcloud': date_range is not None
}
def generate_tv_cloud(search_params,
min_length,
stopwords,
date_range=None,
stems=False,
idf_timeframe=''):
"""
Generates multiple document word clouds using the termvector approach.
"""
# Date range is either provided (in case of burst clouds from the timelines) or from the Query
dates = date_range or search_params['dates']
# First, count the search results
result = count_search_results(settings.ES_INDEX, settings.ES_DOCTYPE,
search_params['query'], dates,
search_params['exclude_distributions'],
search_params['exclude_article_types'],
search_params['selected_pillars'])
doc_count = result.get('count')
update_task_status(0, doc_count)
# Then, create the word clouds per chunk
progress = 0
wordcloud_counter = Counter()
for subset in document_id_chunks(settings.QUERY_DATA_CHUNK_SIZE,
settings.ES_INDEX, settings.ES_DOCTYPE,
search_params['query'], dates,
search_params['exclude_distributions'],
search_params['exclude_article_types'],
search_params['selected_pillars']):
wordcloud_counter += termvector_wordcloud(settings.ES_INDEX,
settings.ES_DOCTYPE, subset,
min_length, stems)
# Update the task status
progress += len(subset)
update_task_status(progress, doc_count)
# Remove non-frequent words form the counter
for key, count in dropwhile(lambda c: c[1] > math.log10(doc_count),
wordcloud_counter.most_common()):
del wordcloud_counter[key]
# Remove the stopwords from the counter
for sw in stopwords:
del wordcloud_counter[sw]
# Return a dictionary with the results
return {
'result': normalize_cloud(wordcloud_counter, idf_timeframe),
'status': 'ok',
'burstcloud': date_range is not None
}
def generate_tv_cloud(search_params, min_length, stopwords, date_range=None, stems=False):
"""Generates multiple document word clouds using the termvector approach"""
chunk_size = settings.QUERY_DATA_CHUNK_SIZE
progress = 0
wordcloud_counter = Counter()
# Date range is either provided or from the Query
dates = date_range or search_params['dates']
# First, count the search results
result = count_search_results(settings.ES_INDEX,
settings.ES_DOCTYPE,
search_params['query'],
dates,
search_params['exclude_distributions'],
search_params['exclude_article_types'],
search_params['selected_pillars'])
doc_count = result.get('count')
info = {
'current': 0,
'total': doc_count
}
current_task.update_state(state='PROGRESS', meta=info)
# Then, create the word clouds per chunk
for subset in document_id_chunks(chunk_size,
settings.ES_INDEX,
settings.ES_DOCTYPE,
search_params['query'],
dates,
search_params['exclude_distributions'],
search_params['exclude_article_types'],
search_params['selected_pillars']):
result = termvector_wordcloud(settings.ES_INDEX,
settings.ES_DOCTYPE,
subset,
min_length,
stems)
wordcloud_counter = wordcloud_counter + result
progress += len(subset)
info = {
'current': progress,
'total': doc_count
}
current_task.update_state(state='PROGRESS', meta=info)
burst = date_range is not None
return counter2wordclouddata(wordcloud_counter, burst, stopwords)
def handle(self, *args, **options):
print 'Emptying table...'
Term.objects.all().delete()
for timeframe, dates in TIMEFRAMES.items():
print 'Retrieving documents for timeframe {}...'.format(timeframe)
exclude_dist = Distribution.objects.exclude(name='Landelijk').values_list('id', flat=True)
date_range = daterange2dates(dates)
total_documents = count_search_results(settings.ES_INDEX,
settings.ES_DOCTYPE,
None,
date_range,
exclude_dist, [], []).get('count')
print 'Total documents: {}'.format(total_documents)
sets = document_id_chunks(10000,
settings.ES_INDEX,
settings.ES_DOCTYPE,
None,
date_range,
dist=exclude_dist)
print 'Counting terms...'
counter = Counter()
for n, s in enumerate(sets):
start_time = time.time()
counter += termvector_wordcloud(settings.ES_INDEX,
settings.ES_DOCTYPE,
s,
min_length=2,
add_freqs=False)
print 'Completed set {} in {} seconds...'.format(n + 1, time.time() - start_time)
print 'Calculating IDFs...'
terms = []
for term, count in counter.items():
if count > 1: # don't add single occurrences
idf = math.log10(total_documents / float(count))
terms.append(Term(timeframe=timeframe, word=term, count=count, idf=idf))
print 'Transferring to database...'
Term.objects.bulk_create(terms, batch_size=10000)
print 'Creating RecordDAWG'
d = dawg.RecordDAWG('<d', zip([t.word for t in terms], [(t.idf,) for t in terms]))
d.save(os.path.join(settings.PROJECT_PARENT, timeframe + '.dawg'))
""" Test code below.
def generate_tv_cloud(search_params, min_length, stopwords, ids=None, stems=False):
"""Generates multiple document word clouds using the termvector approach"""
burst = True
chunk_size = 1000
progress = 0
wordcloud_counter = Counter()
if not ids:
# Normal (non-time line) wordcloud (based on query)
burst = False
result = count_search_results(settings.ES_INDEX,
settings.ES_DOCTYPE,
search_params['query'],
search_params['dates'],
search_params['distributions'],
search_params['article_types'],
search_params['pillars'])
doc_count = result.get('count')
info = {
'current': 0,
'total': doc_count
}
current_task.update_state(state='PROGRESS', meta=info)
for subset in document_id_chunks(chunk_size,
settings.ES_INDEX,
settings.ES_DOCTYPE,
search_params['query'],
search_params['dates'],
search_params['distributions'],
search_params['article_types'],
search_params['pillars']):
result = termvector_wordcloud(settings.ES_INDEX,
settings.ES_DOCTYPE,
subset,
min_length,
stems)
wordcloud_counter = wordcloud_counter + result
progress += len(subset)
info = {
'current': progress,
'total': doc_count
}
current_task.update_state(state='PROGRESS', meta=info)
else:
# Time line word cloud (based in list of document ids)
for subset in utils.chunks(ids, chunk_size):
result = termvector_wordcloud(settings.ES_INDEX,
settings.ES_DOCTYPE,
subset,
min_length,
stems)
wordcloud_counter = wordcloud_counter + result
progress += len(subset)
info = {
'current': progress,
'total': len(ids)
}
current_task.update_state(state='PROGRESS', meta=info)
return counter2wordclouddata(wordcloud_counter, burst, stopwords)